|
|
Description:
|
An
introduction to software
concepts and implementation,
emphasizing problem solving
through abstraction and
decomposition. Introduces
processes and algorithms,
procedural abstraction, data
abstraction, encapsulation,
and object-oriented
programming. Recursion,
iteration, and simple data
structures are covered.
Concepts and skills are
mastered through programming
projects, many of which
employ graphics to enhance
conceptual understanding.
Java, an object-oriented
programming language, is the
vehicle of exploration.
Active-learning sessions are
conducted in a studio
setting in which students
interact with each other and
the professor to solve
problems collaboratively.
Prerequisites: Comfort with
algebra and geometry at the
high school level is
assumed. Patience, good
planning, and organization
will promote success.
Evening exams at which
attendance is required will
be held on 9/24, 11/5, and
11/23 from 6:30-8:30 p.m.
|
|
|
|
11 |
M-W---- |
1:00P-2:20P
|
Urbauer
/ 222 |
Orr
|
No final
|
0 |
0 |
89
|
|
Desc:
|
Waits
managed
by dept.
|
|
|
|
|
|
|
|
|
12 |
M-W---- |
1:00P-2:20P
|
Urbauer
/ 218 |
Orr
|
No final
|
0 |
0 |
73
|
|
Desc:
|
Waits
managed
by dept.
|
|
|
|
|
|
|
|
|
|
|
Description:
|
This
course explores elementary
principles for designing,
creating and publishing
effective websites and web
application front-ends.
Topics include page layout
concepts, color theory,
design principles, search
engine optimization, HTML,
CSS, Javascript, front-end
frameworks like Angular and
React, and other development
tools. Students apply the
topics by creating a series
of websites, which are
judged based on their design
and implementation.
Prerequisite: CSE131 or
equivalent experience
|
|
|
|
Description:
|
For
a very long time, the Things
in our world have lived
relatively lonely and
single-purposed lives. With
the advent of the Internet
of Things, we can address,
control, and interconnect
these formerly isolated
devices to create new and
interesting applications. In
this course we use Wi-Fi,
one of the fundamental
networking technologies
behind Internet-of-Things
devices, and Appcessories,
which include smart watches,
health monitors, toys, and
appliances. In addition to
learning about IoT stacks,
students gain hands-on
experience developing
multi-platform solutions
that control and communicate
with Things using an
accompanying web app.
Students apply their
knowledge and skill to
develop a project of their
choosing using topics from
the course.
Prerequisite: CSE 132
|
|
|
|
01 |
M-W---- |
2:30P-3:50P
|
TBA |
Siever
|
Paper/Project/TakeHome |
60
|
60
|
16
|
|
|
|
|
|
|
|
|
|
01 |
-T-R--- |
10:00A-11:20A |
TBA |
Garnett
|
No final
|
115
|
103
|
0 |
|
|
|
|
|
|
|
02 |
-T-R--- |
2:30P-3:50P
|
TBA |
Garnett
|
No final
|
100
|
100
|
1 |
|
|
|
|
|
|
|
|
|
01 |
-T----- |
2:30P-3:50P
|
TBA |
Cole,
Siever
|
Dec 9 2020
10:00AM - 12:00PM |
245
|
209
|
0 |
|
|
|
|
|
|
|
02 |
-T----- |
4:00P-5:20P
|
TBA |
Siever
|
Dec 9 2020
10:00AM - 12:00PM |
235
|
128
|
0 |
|
|
|
|
|
|
|
A |
---R--- |
11:30A-12:50P |
TBA |
Cole
|
Default -
none |
96
|
56
|
0 |
|
|
|
|
|
|
|
B |
---R--- |
1:00P-2:20P
|
TBA |
Cole
|
Default -
none |
96
|
62
|
0 |
|
|
|
|
|
|
|
|
|
01 |
----F-- |
10:00A-11:50A |
TBA |
Siever
|
No final
|
70
|
26
|
0 |
|
Desc:
|
This
semester
we will
explore
problems
that
arise in
coding
interviews,
with the
goal of
reinforcing
and
augmenting
the
intellectual
material
of 247.
Based on
the
best-selling
book
Cracking
the
Coding
Interview,
students
will
work
sometimes
in teams
and
sometimes
on their
own to
solve
these
problems.
Clarity
and
efficiency
of
solutions
are
emphasized,
and
students
present
their
solutions
in small
groups
and to
the
entire
group.
We plan
to offer
several
time
slots
for this
seminar,
based on
student
availability,
and
those
times
will be
determined
at the
start of
the
semester.
|
|
|
|
|
|
|
|
|
02 |
------S |
12:00P-2:00P
|
TBA |
Siever
|
No final
|
70
|
27
|
0 |
|
Desc:
|
This
semester
we will
explore
problems
that
arise in
coding
interviews,
with the
goal of
reinforcing
and
augmenting
the
intellectual
material
of 247.
Based on
the
best-selling
book
Cracking
the
Coding
Interview,
students
will
work
sometimes
in teams
and
sometimes
on their
own to
solve
these
problems.
Clarity
and
efficiency
of
solutions
are
emphasized,
and
students
present
their
solutions
in small
groups
and to
the
entire
group.
We plan
to offer
several
time
slots
for this
seminar,
based on
student
availability,
and
those
times
will be
determined
at the
start of
the
semester.
|
|
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
1:00P-2:20P
|
TBA |
Richard,
Siever
|
Dec 16 2020
1:00PM - 3:00PM |
70
|
56
|
0 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
10:00A-11:20A |
TBA |
Shidal
|
Dec 14 2020
10:30AM - 12:30PM |
90
|
83
|
0 |
|
|
|
|
|
|
|
02 |
-T-R--- |
10:00A-11:20A |
TBA |
Shidal
|
Dec 15 2020
6:00PM - 8:00PM |
90
|
90
|
2 |
|
|
|
|
|
|
|
|
|
01 |
--W---- |
2:30P-4:20P
|
TBA |
Buhler,
Cole
|
Dec 14 2020
3:30PM - 5:30PM |
0 |
106
|
106
|
|
Desc:
|
Waitlists
are
managed
by the
department.
|
|
|
|
|
|
|
|
|
A |
----F-- |
1:00P-1:50P
|
TBA |
Buhler,
Cole
|
Default -
none |
0 |
51
|
61
|
|
|
|
|
|
|
|
B |
----F-- |
2:00P-2:50P
|
TBA |
Buhler,
Cole
|
Default -
none |
0 |
33
|
28
|
|
|
|
|
|
|
|
C |
----F-- |
3:00P-3:50P
|
TBA |
Buhler,
Cole
|
Default -
none |
0 |
22
|
16
|
|
|
|
|
|
|
|
|
|
Description:
|
Introduction
to the hardware and software
foundations of computer
processing systems. This
course provides a
programmer's perspective of
how computer systems execute
programs and store
information. The course
material aims to enables
students to become more
effective programmers,
especially in dealing with
issues of performance,
portability and robustness.
It also serves as a
foundation for other system
courses, such as compilers,
networks, and operating
systems, where a deeper
understanding of
systems-level issues is
required. Topics covered
include: machine-level code
and its generation by
optimizing compilers,
performance evaluation and
optimization, computer
arithmetic, memory
organization and management,
and supporting concurrent
computation.
Prerequisite: CSE 132
|
|
|
|
01 |
-T-R--- |
11:30A-12:50P |
TBA |
Richard
|
Dec 14 2020
1:00PM - 3:00PM |
50
|
33
|
0 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
10:00A-11:20A |
TBA |
Vorobeychik
|
No final
|
0 |
41
|
73
|
|
Desc:
|
Wait
list
managed
by
department.
|
|
|
|
|
|
|
|
|
|
|
Description:
|
This
course examines complex
systems through the eyes of
a computer scientist. We
will use the representative
power of graphs to model
networks of social,
technological, or biological
interactions. Network
analysis provides many
computational, algorithmic,
and modeling challenges. We
begin by studying graph
theory, allowing us to
quantify the structure and
interactions of social and
other networks. We will then
explore how to practically
analyze network data and how
to reason about it through
mathematical models of
network structure and
evolution. We will also
investigate algorithms that
extract basic properties of
networks in order to find
communities and infer node
properties. Finally, we will
study a range of
applications including
robustness and fragility of
networks such as the
internet, spreading
processes used to study
epidemiology or viral
marketing, and the ranking
of webpages based on the
structure of the webgraph.
This course combines
concepts from computer
science and applied
mathematics to study
networked systems using data
mining.
Prerequisites: CSE 247, ESE
326, MATH 309, and
programming experience
(note: we will parse data
and analyze networks using
Python) |
|
|
|
01 |
M-W---- |
11:30A-12:50P |
TBA |
Venkatesaramani
|
Paper/Project/TakeHome |
40
|
40
|
17
|
|
|
|
|
|
|
|
|
|
Description:
|
The
field of machine learning is
concerned with the question
of how to construct computer
programs that automatically
improve with experience.
This course is a broad
introduction to machine
learning, covering the
foundations of supervised
learning and important
supervised learning
algorithms. Topics to be
covered are the theory of
generalization (including
VC-dimension, the
bias-variance tradeoff,
validation, and
regularization) and linear
and non-linear learning
models (including linear and
logistic regression,
decision trees, ensemble
methods, neural networks,
nearest-neighbor methods,
and support vector
machines). There will be two
in-class exams, one in early
October (tentatively October
10th), and one on the last
day of class, December 5th.
Prerequisites: CSE 247, ESE
326, Math 233, and Math 309
(can be taken
concurrently). |
|
|
|
01 |
-T-R--- |
2:30P-3:50P
|
TBA |
Raviv
|
No final
|
0 |
69
|
134
|
|
Desc:
|
Wait
list
managed
by dept.
|
|
|
|
|
|
|
|
|
|
|
Description:
|
This
course involves a hands-on
exploration of core OS
abstractions, mechanisms and
policies in the context of
the Linux kernel. Readings,
lecture material, studio
exercises, and lab
assignments are closely
integrated in an
active-learning environment
in which students gain
experience and proficiency
using and writing OS code.
Topics include: system
calls, inter-process
communication, interrupt
handling, kernel modules,
concurrency and
synchronization, I/O
facilities, memory
management, virtual memory,
device management, and file
system organization.
Prerequisite: CSE 361S.
|
|
|
|
01 |
----F-- |
10:00A-11:20A |
TBA |
Cole
|
No final
|
30
|
15
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
Secure
computing requires the
secure design,
implementation, and use of
systems and algorithms
across many areas of
computer science.
Fundamentals of secure
computing such as trust
models and cryptography will
lay the groundwork for
studying key topics in the
security of systems,
networking, web design,
machine learning algorithms,
mobile applications, and
physical devices. Human
factors, privacy, and the
law will also be considered.
Hands-on practice exploring
vulnerabilities and defenses
using Linux, C, and Python
in studios and lab
assignments is a key
component of the course.
Prerequisites: CSE 247 and
either CSE 361 or CSE
332. |
|
|
|
01 |
-T-R--- |
11:30A-12:50P |
TBA |
Cole,
Zhang
|
Dec 10 2020
10:30AM - 12:30PM |
40
|
40
|
12
|
|
|
|
|
|
|
|
02 |
-T-R--- |
10:00A-11:20A |
TBA |
Cole,
Zhang
|
Dec 10 2020
10:30AM - 12:30PM |
40
|
40
|
7 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
11:30A-12:50P |
TBA |
Sproull
|
Dec 15 2020
10:30AM - 12:30PM |
162
|
162
|
29
|
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
2:30P-3:50P
|
TBA |
Garnett
|
Dec 14 2020
3:30PM - 5:30PM |
20
|
20
|
11
|
|
|
|
|
|
|
|
|
|
01 |
-T-R--- |
11:30A-12:50P |
TBA |
Ottley
|
Dec 14 2020
1:00PM - 3:00PM |
0 |
0 |
89
|
|
|
|
|
|
|
|
|
|
Description:
|
Introduces
the issues, challenges, and
methods for designing
embedded computing systems -
systems designed to serve a
particular application,
which incorporate the use of
digital processing devices.
Examples of embedded systems
include PDAs, cellular
phones, appliances, game
consoles, automobiles, and
iPod. Emphasis is given to
aspects of design that are
distinct to embedded
systems. The course examines
hardware, software, and
system-level design.
Hardware topics include
microcontrollers, digital
signal processors, memory
hierarchy, and I/O. Software
issues include languages,
run-time environments, and
program analysis.
System-level topics include
real-time operating systems,
scheduling, power
management, and wireless
sensor networks. Students
will perform a course
project on a real wireless
sensor network testbed.
Prerequisites: CSE 361S.
|
|
|
|
01 |
-T-R--- |
5:30P-7:00P
|
TBA |
Ivanovich
|
Dec 15 2020
6:00PM - 8:00PM |
30
|
20
|
0 |
|
|
|
|
|
|
|
|
|
01 |
TBA |
|
TBA |
Guerin
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
02 |
TBA |
|
TBA |
Das
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
04 |
TBA |
|
TBA |
Brent
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
05 |
TBA |
|
TBA |
Agrawal
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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|
|
06 |
TBA |
|
TBA |
Baruah
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
07 |
TBA |
|
TBA |
Gill
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
08 |
TBA |
|
TBA |
Cole
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
09 |
TBA |
|
TBA |
Buhler
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
10 |
TBA |
|
TBA |
Ottley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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|
|
11 |
TBA |
|
TBA |
Shook
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
12 |
TBA |
|
TBA |
Lu
|
Default -
none |
0 |
0 |
0 |
|
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|
13 |
TBA |
|
TBA |
Siever
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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|
|
15 |
TBA |
|
TBA |
Sproull
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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16 |
TBA |
|
TBA |
Juba
|
Default -
none |
0 |
0 |
0 |
|
|
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17 |
TBA |
|
TBA |
Kelleher
|
Default -
none |
0 |
0 |
0 |
|
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18 |
TBA |
|
TBA |
Neumann
|
Default -
none |
0 |
0 |
0 |
|
|
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19 |
TBA |
|
TBA |
Lee
|
Default -
none |
0 |
0 |
0 |
|
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20 |
TBA |
|
TBA |
Richard
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
22 |
TBA |
|
TBA |
Cosgrove
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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24 |
TBA |
|
TBA |
Crowley
|
Default -
none |
0 |
0 |
0 |
|
|
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27 |
TBA |
|
TBA |
Ho
|
Default -
none |
0 |
0 |
0 |
|
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|
|
|
31 |
TBA |
|
TBA |
Buckley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
33 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
34 |
TBA |
|
TBA |
Nehorai
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
35 |
TBA |
|
TBA |
Chen
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
36 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
37 |
TBA |
|
TBA |
Ju
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
38 |
TBA |
|
TBA |
Jain
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
39 |
TBA |
|
TBA |
Barbour
|
Default -
none |
0 |
0 |
1 |
|
|
|
|
|
|
|
40 |
TBA |
|
TBA |
Shidal
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
43 |
TBA |
|
TBA |
Stormo
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
44 |
TBA |
|
TBA |
Lai
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
45 |
TBA |
|
TBA |
Cytron
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
46 |
TBA |
|
TBA |
Wang
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
47 |
TBA |
|
TBA |
Payne
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
49 |
TBA |
|
TBA |
Yeoh
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
50 |
TBA |
|
TBA |
Kamilov
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
52 |
TBA |
|
TBA |
Hugo
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
|
|
Description:
|
An
introduction to software
concepts and implementation,
emphasizing problem solving
through abstraction and
decomposition. Introduces
processes and algorithms,
procedural abstraction, data
abstraction, encapsulation,
and object-oriented
programming. Recursion,
iteration, and simple data
structures are covered.
Concepts and skills are
mastered through programming
projects, many of which
employ graphics to enhance
conceptual understanding.
Java, an object-oriented
programming language, is the
vehicle of exploration.
Active-learning sessions are
conducted in a studio
setting in which students
interact with each other and
the professor to solve
problems collaboratively.
Prerequisites: Comfort with
algebra and geometry at the
high school level is
assumed. Patience, good
planning, and organization
will promote success.
Evening exams at which
attendance is required will
be held on 9/24, 11/5, and
11/23 from 6:30-8:30 p.m.
|
|
|
|
01 |
-T----- |
2:30P-3:50P
|
TBA |
Cole,
Siever
|
Dec 9 2020
10:00AM - 12:00PM |
245
|
209
|
0 |
|
|
|
|
|
|
|
02 |
-T----- |
4:00P-5:20P
|
TBA |
Siever
|
Dec 9 2020
10:00AM - 12:00PM |
235
|
128
|
0 |
|
|
|
|
|
|
|
A |
---R--- |
11:30A-12:50P |
TBA |
Cole
|
Default -
none |
96
|
56
|
0 |
|
|
|
|
|
|
|
B |
---R--- |
1:00P-2:20P
|
TBA |
Cole
|
Default -
none |
96
|
62
|
0 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
10:00A-11:20A |
TBA |
Shidal
|
Dec 14 2020
10:30AM - 12:30PM |
90
|
83
|
0 |
|
|
|
|
|
|
|
02 |
-T-R--- |
10:00A-11:20A |
TBA |
Shidal
|
Dec 15 2020
6:00PM - 8:00PM |
90
|
90
|
2 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
1:00P-2:20P
|
TBA |
Richard,
Siever
|
Dec 16 2020
1:00PM - 3:00PM |
70
|
56
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
The
discipline of artificial
intelligence (AI) is
concerned with building
systems that think and act
like humans or rationally on
some absolute scale. This
course is an introduction to
the field, with special
emphasis on sound modern
methods. The topics include
knowledge representation,
problem solving via search,
game playing, logical and
probabilistic reasoning,
planning, dynamic
programming, and
reinforcement learning.
Programming exercises
concretize the key methods.
The course targets graduate
students and advanced
undergraduates. Evaluation
is based on written and
programming assignments, a
midterm exam and a final
exam. Prerequisites: CSE
247, ESE 326, Math 233
|
|
|
|
01 |
-T-R--- |
1:00P-2:20P
|
TBA |
Yeoh
|
Dec 15 2020
1:00PM - 3:00PM |
0 |
143
|
11
|
|
Desc:
|
Waits
managed
by
department
|
|
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
2:30P-3:50P
|
TBA |
Zhang,
Weixiong |
Dec 14 2020
3:30PM - 5:30PM |
100
|
70
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
This
course will cover machine
learning from a Bayesian
probabilistic perspective.
Bayesian probability allows
us to model and reason about
all types of uncertainty.
The result is a powerful,
consistent framework for
approaching many problems
that arise in machine
learning, including
parameter estimation, model
comparison, and decision
making. We will begin with a
high-level introduction to
Bayesian inference, then
proceed to cover
more-advanced topics. These
will include inference
techniques (exact, MAP,
sampling methods, the
Laplace approximation,
etc.), Bayesian decision
theory, Bayesian model
comparison, Bayesian
nonparametrics, and Bayesian
optimization. Prerequisites:
CSE 417T, ESE 326. |
|
|
|
Description:
|
This
course introduces the
fundamental techniques and
concepts needed to study
multi-agent systems, in
which multiple autonomous
entities with different
information sets and goals
interact. We will study
algorithmic, mathematical,
and game-theoretic
foundations, and how these
foundations can help us
understand and design
systems ranging from robot
teams to online markets to
social computing platforms.
Topics covered may include
game theory, distributed
optimization, multi-agent
learning and
decision-making, preference
elicitation and aggregation,
mechanism design, and
incentives in social
computing systems.
Prerequisites: CSE 347 (may
be taken concurrently), ESE
326 (or Math 3200), and Math
233 or equivalents. Some
prior exposure to artificial
intelligence, machine
learning, game theory, and
microeconomics may be
helpful, but is not
required. |
|
|
|
01 |
-T-R--- |
1:00P-2:20P
|
TBA |
Das
|
No final
|
0 |
0 |
61
|
|
|
|
|
|
|
|
|
|
Description:
|
This
course is an exploration of
the opportunities and
challenges of
human-in-the-loop
computation, an emerging
field that examines how
humans and computers can
work together to solve
problems neither can solve
alone yet. We will explore
ways in which techniques
from machine learning, game
theory, optimization, online
behavioral social science,
and human-computer
interactions can be used to
model and analyze
human-in-the-loop systems
such as crowdsourcing
markets, prediction markets,
and user-generated content
platforms. We will also look
into recent developments in
the interactions between
humans and AIs, such as
learning with the presence
of strategic behavior and
ethical issues in AI
systems. Prerequisites: CSE
247, ESE 326, Math 233, and
Math 309. |
|
|
|
01 |
-T-R--- |
4:00P-5:20P
|
TBA |
Ho
|
Paper/Project/TakeHome |
20
|
20
|
33
|
|
|
|
|
|
|
|
|
|
01 |
-T-R--- |
1:00P-2:20P
|
TBA |
Lu
|
No final
|
45
|
28
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
This
course offers an in-depth
hands-on exploration of core
OS abstractions, mechanisms
and policies in the context
of the Linux kernel, with an
increasing focus on
profiling, understanding,
and evaluating their
behaviors and interactions.
Readings, lecture material,
studio exercises, and lab
assignments are closely
integrated in an
active-learning environment
in which students gain
experience and proficiency
writing, tracing, and
evaluating user-space and
kernel-space code. Topics
include: memory forensics,
file-system forensics,
timing forensics, process
and thread forensics, and
managing internal or
external causes of anomalous
behavior, in different
settings (which may vary
each semester) such as
virtualization and real-time
systems. Prerequisite: CSE
422S. |
|
|
|
01 |
M-W---- |
10:00A-11:20A |
Urbauer
/ 218 |
Orr
|
Dec 14 2020
10:30AM - 12:30PM |
30
|
15
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
Large-scale
optimization is an essential
component of modern data
science, artificial
intelligence, and machine
learning. This
graduate-level course
rigorously introduces
optimization methods that
are suitable for large-scale
problems arising in these
areas. We will learn several
algorithms suitable for both
smooth and nonsmooth
optimization, including
gradient methods, proximal
methods, mirror descent,
Nesterov's acceleration,
ADMM, quasi-Newton methods,
stochastic optimization,
variance reduction, as well
as distributed optimization.
Throughout the class, we
will discuss the efficacy of
these methods in concrete
data science problems, under
appropriate statistical
models. Students will be
required to program in
python or MATLAB.
Prerequisites: CSE 247, Math
309, Math 3200 or ESE
326. |
|
|
|
01 |
M-W---- |
1:00P-2:20P
|
TBA |
Kamilov
|
Dec 16 2020
1:00PM - 3:00PM |
30
|
30
|
64
|
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
4:00P-5:20P
|
TBA |
Guerin
|
Dec 11 2020
6:00PM - 8:00PM |
20
|
20
|
0 |
|
|
|
|
|
|
|
|
|
01 |
-T-R--- |
10:00A-11:20A |
TBA |
Baruah
|
Dec 15 2020
6:00PM - 8:00PM |
70
|
49
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
The
course will provide an
in-depth coverage of modern
algorithms for the numerical
solution of multidimensional
optimization problems.
Unconstrained optimization
techniques including
Gradient methods, Newton's
methods, Quasi-Newton
methods, and conjugate
methods will be introduced.
The emphasis is on
constrained optimization
techniques: Lagrange theory,
Lagrangian methods, penalty
methods, sequential
quadratic programming,
primal-dual methods, duality
theory, nondifferentiable
dual methods, and
decomposition methods. The
course will also discuss
applications in engineering
systems and use of
state-of-the-art computer
codes. Special topics may
include large-scale systems,
parallel optimization, and
convex optimization.
Prerequisites: Calculus I
and Math 309 |
|
|
|
01 |
-T-R--- |
1:00P-2:20P
|
TBA |
Chen
|
Dec 15 2020
1:00PM - 3:00PM |
90
|
34
|
0 |
|
|
|
|
|
|
|
|
|
Description:
|
This
course is designed to
introduce graduate
engineering students to
research at the intersection
of engineered algorithms
(such as estimators,
detectors, classifiers, and
control systems) and the
problems of bias,
unfairness, inequity, and
oppression. The objective is
both to use engineering
tools to analyze and
quantify problems, as well
as to study the problems
introduced by engineering
tools including detectors,
estimators, classifiers, and
control systems. Topics
include critical race and
feminist theory, measurement
theory, estimation bias,
limitations on detection
fairness, and random process
models used for control
systems. |
|
|
|
01 |
-T-R--- |
2:30P-3:50P
|
TBA |
Patwari
|
See
instructor |
999
|
0 |
0 |
|
|
|
|
|
|
|
01 |
-T-R--- |
4:00P-5:20P
|
TBA |
Juba
|
Dec 16 2020
6:00PM - 8:00PM |
60
|
27
|
0 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
1:00P-2:20P
|
TBA |
Agrawal
|
Dec 16 2020
1:00PM - 3:00PM |
40
|
40
|
3 |
|
|
|
|
|
|
|
|
|
Description:
|
With
the advance of imaging
technologies deployed in
medicine, engineering and
science, there is a rapidly
increasing amount of spatial
data sets (images, volumes,
point clouds, etc.) that
need to be processed,
visualized, and analyzed.
This course will focus on a
number of geometry-related
computing problems that are
essential in the knowledge
discovery process in various
spatial-data-driven
biomedical applications.
These problems include
visualization, segmentation,
mesh construction and
processing, shape
representation and analysis.
The course consists of
lectures that cover theories
and algorithms, and a series
of hands-on programming
projects using real-world
data collected by various
imaging techniques (CT, MRI,
electron cryo-microscopy,
etc.). Prerequisite: CSE332
(or proficiency in
programming in C++ or Java
or Python) and CSE247.
|
|
|
|
01 |
-T-R--- |
2:30P-3:50P
|
TBA |
Ju
|
Dec 16 2020
3:30PM - 5:30PM |
60
|
39
|
0 |
|
|
|
|
|
|
|
|
|
01 |
M-W---- |
4:00P-5:20P
|
TBA |
Kelleher
|
Dec 11 2020
6:00PM - 8:00PM |
40
|
40
|
7 |
|
|
|
|
|
|
|
|
|
01 |
-T-R--- |
11:30A-12:50P |
TBA |
Chakrabarti
|
No final
|
0 |
40
|
52
|
|
|
|
|
|
|
|
|
|
Description:
|
First
course in wireless
networking providing a
comprehensive treatment of
wireless data and
telecommunication networks.
Topics include recent trends
in wireless and mobile
networking, wireless coding
and modulation, wireless
signal propagation, IEEE
802.11a/b/g/n/ac wireless
local area networks, 60 GHz
millimeter wave gigabit
wireless networks, vehicular
wireless networks, white
spaces, IEEE 802.22 regional
area networks, Bluetooth and
Bluetooth Smart, wireless
personal area networks,
wireless protocols for
Internet of Things, ZigBee,
cellular networks: 1G/2G/3G,
LTE, LTE-Advanced, and 5G.
Prerequisites: CSE 473S or
permission of the
instructor. |
|
|
|
01 |
M-W---- |
1:00P-2:20P
|
TBA |
Jain
|
Dec 16 2020
1:00PM - 3:00PM |
30
|
10
|
0 |
|
|
|
|
|
|
|
|
|
01 |
----F-- |
3:00P-3:50P
|
TBA |
Chen
|
No final
|
30
|
3 |
0 |
|
|
|
|
|
|
|
|
|
01 |
TBA |
|
TBA |
Guerin
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
02 |
TBA |
|
TBA |
Das
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
04 |
TBA |
|
TBA |
Brent
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
05 |
TBA |
|
TBA |
Agrawal
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
06 |
TBA |
|
TBA |
Baruah
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
07 |
TBA |
|
TBA |
Gill
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
08 |
TBA |
|
TBA |
Cole
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
09 |
TBA |
|
TBA |
Buhler
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
10 |
TBA |
|
TBA |
Ottley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
11 |
TBA |
|
TBA |
Shook
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
12 |
TBA |
|
TBA |
Lu
|
Default -
none |
0 |
3 |
0 |
|
|
|
|
|
|
|
13 |
TBA |
|
TBA |
Siever
|
Default -
none |
0 |
1 |
0 |
|
|
|
|
|
|
|
15 |
TBA |
|
TBA |
Sproull
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
16 |
TBA |
|
TBA |
Juba
|
Default -
none |
0 |
0 |
0 |
|
|
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|
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|
|
17 |
TBA |
|
TBA |
Kelleher
|
Default -
none |
0 |
0 |
0 |
|
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|
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|
18 |
TBA |
|
TBA |
Neumann
|
Default -
none |
0 |
0 |
0 |
|
|
|
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|
19 |
TBA |
|
TBA |
Lee
|
Default -
none |
0 |
0 |
0 |
|
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|
20 |
TBA |
|
TBA |
Richard
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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21 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
1 |
|
|
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22 |
TBA |
|
TBA |
Cosgrove
|
Default -
none |
0 |
0 |
1 |
|
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24 |
TBA |
|
TBA |
Crowley
|
Default -
none |
0 |
0 |
0 |
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27 |
TBA |
|
TBA |
Ho
|
Default -
none |
0 |
0 |
0 |
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31 |
TBA |
|
TBA |
Buckley
|
Default -
none |
0 |
0 |
0 |
|
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33 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
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34 |
TBA |
|
TBA |
Nehorai
|
Default -
none |
0 |
0 |
0 |
|
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35 |
TBA |
|
TBA |
Chen
|
Default -
none |
0 |
0 |
0 |
|
|
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|
36 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
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|
37 |
TBA |
|
TBA |
Ju
|
Default -
none |
0 |
0 |
0 |
|
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|
38 |
TBA |
|
TBA |
Jain
|
Default -
none |
0 |
0 |
0 |
|
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|
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|
39 |
TBA |
|
TBA |
Barbour
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
40 |
TBA |
|
TBA |
Shidal
|
Default -
none |
0 |
0 |
1 |
|
|
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|
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|
43 |
TBA |
|
TBA |
Stormo
|
Default -
none |
0 |
0 |
0 |
|
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|
|
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|
44 |
TBA |
|
TBA |
Lai
|
Default -
none |
0 |
0 |
0 |
|
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45 |
TBA |
|
TBA |
Cytron
|
Default -
none |
0 |
0 |
1 |
|
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46 |
TBA |
|
TBA |
Wang
|
Default -
none |
0 |
0 |
0 |
|
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|
|
|
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|
47 |
TBA |
|
TBA |
Payne
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
49 |
TBA |
|
TBA |
Yeoh
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
50 |
TBA |
|
TBA |
Kamilov
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
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|
52 |
TBA |
|
TBA |
Hugo
|
Default -
none |
0 |
0 |
0 |
|
|
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|
|
01 |
TBA |
|
TBA |
Guerin
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
02 |
TBA |
|
TBA |
Das
|
Default -
none |
0 |
0 |
0 |
|
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|
|
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|
04 |
TBA |
|
TBA |
Brent
|
Default -
none |
0 |
0 |
0 |
|
|
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|
|
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|
05 |
TBA |
|
TBA |
Agrawal
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
06 |
TBA |
|
TBA |
Baruah
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
07 |
TBA |
|
TBA |
Gill
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
08 |
TBA |
|
TBA |
Cole
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
09 |
TBA |
|
TBA |
Buhler
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
10 |
TBA |
|
TBA |
Ottley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
11 |
TBA |
|
TBA |
Shook
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
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|
12 |
TBA |
|
TBA |
Lu
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
13 |
TBA |
|
TBA |
Siever
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
15 |
TBA |
|
TBA |
Sproull
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
16 |
TBA |
|
TBA |
Juba
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
17 |
TBA |
|
TBA |
Kelleher
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
18 |
TBA |
|
TBA |
Neumann
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
19 |
TBA |
|
TBA |
Lee
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
20 |
TBA |
|
TBA |
Richard
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
21 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
22 |
TBA |
|
TBA |
Cosgrove
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
24 |
TBA |
|
TBA |
Crowley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
27 |
TBA |
|
TBA |
Ho
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
31 |
TBA |
|
TBA |
Buckley
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
33 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
34 |
TBA |
|
TBA |
Nehorai
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
35 |
TBA |
|
TBA |
Chen
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
36 |
TBA |
|
TBA |
[TBA] |
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
37 |
TBA |
|
TBA |
Ju
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
38 |
TBA |
|
TBA |
Jain
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
39 |
TBA |
|
TBA |
Barbour
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
40 |
TBA |
|
TBA |
Shidal
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
43 |
TBA |
|
TBA |
Stormo
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
44 |
TBA |
|
TBA |
Lai
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
45 |
TBA |
|
TBA |
Cytron
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
46 |
TBA |
|
TBA |
Wang
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
47 |
TBA |
|
TBA |
Payne
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
49 |
TBA |
|
TBA |
Yeoh
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
50 |
TBA |
|
TBA |
Kamilov
|
Default -
none |
0 |
0 |
2 |
|
|
|
|
|
|
|
52 |
TBA |
|
TBA |
Hugo
|
Default -
none |
0 |
0 |
0 |
|
|
|
|
|
|
|
|
|
01 |
TBA |
|
TBA |
Guerin
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
02 |
TBA |
|
TBA |
Das
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
04 |
TBA |
|
TBA |
Brent
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
05 |
TBA |
|
TBA |
Agrawal
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
06 |
TBA |
|
TBA |
Baruah
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
07 |
TBA |
|
TBA |
Gill
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
08 |
TBA |
|
TBA |
Cole
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
09 |
TBA |
|
TBA |
Buhler
|
Default -
none |
999
|
2 |
0 |
|
|
|
|
|
|
|
10 |
TBA |
|
TBA |
Ottley
|
Default -
none |
999
|
2 |
0 |
|
|
|
|
|
|
|
11 |
TBA |
|
TBA |
Shook
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
12 |
TBA |
|
TBA |
Lu
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
13 |
TBA |
|
TBA |
Siever
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
16 |
TBA |
|
TBA |
Juba
|
Default -
none |
999
|
3 |
0 |
|
|
|
|
|
|
|
17 |
TBA |
|
TBA |
Kelleher
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
18 |
TBA |
|
TBA |
Neumann
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
19 |
TBA |
|
TBA |
Lee
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
20 |
TBA |
|
TBA |
Richard
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
21 |
TBA |
|
TBA |
[TBA] |
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
22 |
TBA |
|
TBA |
Cosgrove
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
24 |
TBA |
|
TBA |
Crowley
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
27 |
TBA |
|
TBA |
Ho
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
31 |
TBA |
|
TBA |
Buckley
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
33 |
TBA |
|
TBA |
[TBA] |
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
34 |
TBA |
|
TBA |
Nehorai
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
35 |
TBA |
|
TBA |
Chen
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
36 |
TBA |
|
TBA |
[TBA] |
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
37 |
TBA |
|
TBA |
Ju
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
38 |
TBA |
|
TBA |
Jain
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
39 |
TBA |
|
TBA |
Barbour
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
40 |
TBA |
|
TBA |
Shidal
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
43 |
TBA |
|
TBA |
Stormo
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
44 |
TBA |
|
TBA |
Lai
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
45 |
TBA |
|
TBA |
Cytron
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
46 |
TBA |
|
TBA |
Wang
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
47 |
TBA |
|
TBA |
Payne
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
49 |
TBA |
|
TBA |
Yeoh
|
Default -
none |
999
|
3 |
0 |
|
|
|
|
|
|
|
50 |
TBA |
|
TBA |
Kamilov
|
Default -
none |
999
|
2 |
0 |
|
|
|
|
|
|
|
52 |
TBA |
|
TBA |
Hugo
|
Default -
none |
999
|
0 |
0 |
|
|
|
|
|
|
|
53 |
TBA |
|
TBA |
Raviv
|
Default -
none |
999
|
1 |
0 |
|
|
|
|
|
|
|
|